Abstract: Symbolic regression (SR) is an important technique for discovering hidden mathematical expressions from observed data. Transformer-based approaches have been widely used for machine translation due to their high performance, and are recently highly expected to be used for SR. They input the data points, then output the expression skeleton, and finally optimize the coefficients. However, recent transformer-based methods for SR focus more attention on large scale training data and ignore the ill-posed problem: the lack of sufficient supervision, i.e., expressions that may be completely different have the same supervision because of their same skeleton, which makes it challenging to deal with data that may be from the same expression skeleton but with different coefficients. Therefore, we present a transformer-based model for SR with the ability to alleviate this problem. Specifically, we leverage a feature extractor based on pure residual MLP networks to obtain more information about data points. Furthermore, the core idea is that we propose a joint learning mechanism combining supervised contrastive learning, which makes features of data points from expressions with the same skeleton more similar so as to effectively alleviates the ill-posed problem. The benchmark results show that the proposed method is up to 25% higher with respect to the recovery rate of skeletons than typical transformer-based methods. Moreover, our method outperforms state-of-the-art SR methods based on reinforcement learning and genetic programming in terms of the coefficient of determination ($R^2$).